Pham, T.H. (2022).

Understanding perceived shopping Pham

effectiveness with omnichannel: A MOA theory
perspective. In Proceedings of The International
Conference on Electronic Business, Volume 22 (pp. xxx-
xxx). ICEB’22, Bangkok, Thailand, October 13-17,
2022.

Understanding Perceived Shopping Effectiveness with Omnichannel: A MOA

Theory Perspective
(Page Count: 9)
Thi-Hoa Pham 1,*
_____________________
*Corresponding author
1
Ho Chi Minh Ctiy University of Banking, Ho Chi Minh, Vietnam, Lecturer, hoapt@buh.edu.vn

ABSTRACT
Customers’ shopping effectiveness is a critical factor in encouraging customers to stay with the firms, the knowledge regarding
how to provide shopping effectiveness in an omnichannel retailing environment remains underexplored. Thus, this study draws
on Motivation-Opportunity-Ability theory (MOA) and examines MOA factors affecting customers’ perceived shopping
effectiveness, which in turn influences customers’ omnichannel usage continuance intention. The expected findings may
suggest that the opportunity factor, channel integration quality encompassing channel-service configuration, content
consistency, process consistency, and assurance quality, positively influences customers’ percieved shopping effectiveness.
Furthermore, the motivation factors such as relative advantage, perceived ease of use, personalized incentives, flow, and
enjoyment, have positive effects on customers’ perceived shopping effectiveness. In addition, ability factors encompassing
technology readiness and self efficacy are expected to influence perceived shopping effectiveness. As a result, perceived
shopping effectiveness positively influences omnichannel usage continuance intention. These findings enhance the literature
on the shopping values and channel integration quality in an omnichannel retailing environment. These findings also offer
insightful implications for omnichannel retailers in terms of creating and managing customers’ shopping effectivess in the
post-COVID period.

Keywords: Omnichannel, shopping effectiveness, MOA, channel integration quality, personalized incentives.

INTRODUCTION
The proliferation of digital technologies leads to an increasing number of companies implementing an omni-channel retailing
strategy to enhance customer shopping experiences (Adivar et al., 2019). Omnichannel retailing refers to retailing that involves
channel integration quality for the purpose of creating a seamless shopping experience for customers, regardless of the channel
or purchasing process stage, which lies at the heart of omnichannel retailing (Cummins et al., 2016). These days, customers no
longer purchase only in-store or online; instead, they shop across channels. They use various channels such as physical stores,
websites, direct mail and catalogs, social media sites, review sites, call centers, mobile devices, kiosks, home services,
networked appliances, so on and so forth to complete a single purchase. For example, they search for information in one
channel, and complete the purchase in another (Asare et al., 2022). Furthermore, the move toward shopping with omnichannel
platforms has created opportunities for omnichannel retailers to improve the holistic consumer shopping experience by adding
more fun, unique incentives, and higher shopping efficacy (Alizila, 2018). For example, omnichannel allows consumers to
experience a shopping environment within a physical store, such as exploring product information that can only be found
through digital channels with the added benefit of touching, feeling, and seeing how the products function, sharing information
with friends, or receiving social network comments from friends. After leaving the store, they can still keep updated with new
product information and sales events within the omnichannel platform. As a result, consumers can enjoy multiple benefits such
as increased efficiency, monetary benefits, novelty, and enjoyment by a greatly enhanced consumer shopping process with
omnichannel. In addition, omnichannel can faciliate personalized collaborative activities with friends to earn rewards, offering
individualized shopping services, push notifications, and rewards, generating unique consumer shopping experience (Lemon &
Verhoef, 2016). IKEA (UK) reported that after making its products pretty much accessible across retailing channels, customers
increasingly used both online and offline channels to complete their purchasing journey, which resulted in a 31% rise in online
sales (Rigby, 2016). International Data Corporation (IDC) found that customers using both online and physical channels have
30% higher lifetime value than those purchasing from a single channel. Many managers have cited the omni-channel strategy
as a top business priority (Shen et al., 2018), as it can increase per order value by 13% on average and produce 90% higher
customer retention rates than a single-channel strategy (Collins, 2019). In China, 85% of apparel shoppers now engage with
omnichannel services to make purchases, up from 80% in 2017 (McKinsey Digital, 2019). Furthermore, COVID-19 pandemic
prompts 90% of younger customers to continue adopting omnichannel services (Oliver Wyman, 2020). In Vietnam, consumers
have shifted towards omni-channel purchasing behaviours in the post-COVID period, resulting in more than 50 per cent of
Vietnamese consumers have reduced their frequency of visits to supermarkets, grocery stores and wet markets, while 25 per
cent of them have increased online shopping (Vietnamnet, 2020).
With huge advantages of omnichannel, it is supposed that omnichannel retailing can be a promising strategy for firms to
increase the customers’ shopping effectiveness. However, limited research has explored the customer perception of shopping

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effectiveness with omnichannel. As it is the main contribution, this study explores the factors that influence the customer
perception of shopping effectiveness with omnichannel. More specifically, the present study uses motivation, opportunity, and
ability (MOA) framework (MacInnis et al., 1991) to examine the factors that determine customers’ perception of shopping
effectiveness with omnichannel. In doing so, this study investigates the role of motivations (relative advantage, perceived ease
of use, flow, and enjoyment, personalized incentives), opportunity (channel integration quality), and ability (self-efficacy,
technology readiness) on shoppers’ perception of shopping effectiveness with omnichannel, which in turn, influence their
omnichannel usge continuance intention.

THEORETICAL BACKGROUND AND HYPOTHESES DEVELOPMENT

Motivation-Opportunity-Ability (MOA) Theory

MOA theory (MacInnis et al., 1991) explains that consumer behaviour is affected by motivation, opportunity, and ability. In
other words, consumer behaviour is considered as a function of the consumer’s willingness to perform a particular behaviour
(motivation) combined with his or her internal capability (ability) and contextual factors (opportunity). Thus, consumers’
behaviour can be proactively managed by controlling the levels of motivation, opportunity, and ability variables. For this
reason, the MOA theory was chosen to examine customer adoption of omnichannel in this study.

Although the omnichannel retailing offers many benefits for customers, a lack of knowledge of its adoption drivers may cause
retailers to struggle to manage it effectively. Thus, an understanding of the factors that drive customers to perceive shopping
effectiveness of omnichannel is essential for retailers in providing insights into customers’adoption of omnichannel. The MOA
framework (MacInnis et al., 1991) is used to theoretically identify and hypothesize variables that influence customers’
perception of shopping effectiveness toward ominchannel. The MOA framework is a well-established theory, which has been
applied to explain behaviours in consumers, employees, and salespeople (Sabnis et al., 2013). this theory assumes that if
motivation, ability, and opportunity can each present a potential determinant of cusomters’ shopping effectiveness with
ominchannel, then retailers can identify specific driving forces and employ appropriate strategies for successful
implementation of omnichannel. Consistent with existing research (e.g., Cui et al., 2020), this study identifies variables that are
relevant for MOA factors for customer adoption of omnichannel. This study used this perspective as using MOA variables
allows a more careful examination as well as considers context-specific factors influencing the customer perception of
shopping effectiveness, which in turn, affects omnichannel usage continuance intention. Particularly, it is hypothesised that
motivation variables encompassing relative advantage, perceived ease of use, personalized incentives, flow, and enjoyment.
Opportunity variables including dimensions of channel integration quality, and ability variables including technology readiness
and self-efficacy directly impact perceived shopping effectiveness. Perceived shopping effectiveness is defined in this study as
the extent to which consumers perceive using omnichannel to improve their overall shopping experience (Roy et al., 2017).
Consequently, perceived shopping effectiveness affects omnichannel usage continuance intention referring to the extent to
which a customer wishes to continue to use omnichannel for shopping (Gao et al., 2021). Figure 1 presents the conceptual
framework of this study.

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Motivation
Motivation refers to the desire or readiness of consumers to accomplish a specific behaviour (MacInnis et al., 1991). As
motivation can stem from either an internally generated desire to participate in an act or an external one that arises from the
performance of the behaviour (Ryan & Deci, 2000). Thus, this study considers intrinsic and extrinsic motivations as two types
of drivers that are more likely to influence the customer perception of shopping effectiveness. Although the omnichannel
approach is much more than a technology, its foundations lie in the capability of using technologies during a single purchase
(Kopot & Cude, 2021), this study identifies relative advantage, perceived ease of use as extrinsic motivations, and flow,
enjoyment, personalized incentives as intrinsic motivations for customer adoption of omnichannel.

Relative advantage is one of the critical extrinsic factors that facilitate the diffusion of innovation (Rogers, 2003). It refers to
the extent to which new technology is perceived as offering advanced features and benefits for customers over existing
technologies. Shopping with omnichannel offers many advanced features and functionalities such as ubiquitous connectivity,
real-time interaction, localised and personalised information, and greater monitoring and support for customers (e.g., shopping
efficiency, saving time and effort in shopping) (Kang, 2019; Asmare & Zewdie, 2022). Thus, this study proposes that these
advantages can result in perceiving superior shopping values of omnichannel. Therefore, it is hypothesized that:

H1: Relative advantage positively influences perceived shopping effectiveness.

Perceived ease of use is defined as the user’s perception of the degree of effort that the user needs to use a specific technology
(Venkatesh, 2000). The perceived ease of use is often considered to be opposite to perceived complexity referring to the degree
to which customers perceive the new technology as relatively difficult to understand and use (Rogers, 2003). Extending this
defnition to the shopping context with omnichannel, perceived ease of use is the degree of ease associated with consumers’ use
of different touchpoints during the shopping process with omnichannel (Juaneda-Ayensa et al., 2016). Balaji and Roy (2017)
demonstrated that the ease of use of smart technology is likely to result in customer perception of its superior functionality.

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Therefore, this study argues that when customers perceive omnichannel as ease of use, they hold a favor of it, which may
shape their perception of shopping efficiency with omnichannel. Thus, this study hypothesizes that:

H2: Perceived ease of use positively influences perceived shopping effectiveness.

Personalized incentives refers to the perceived amount of individualized consumer information, services, rewards, and
incentives given when using omnichannel platforms (Hsia et al., 2020). Modern consumers increasingly embrace personalized
services, thus personalization incentives are widely used in omnichannel retailing (Zhu et al., 2017). For instance, omnichannel
platforms provide consumers with purchase recommendations, shopping guides, location-based services (e.g., indoor
navigation, reference cues, and route maps), real-time personalized rewards in stores or online (e.g., virtual coupons, red
envelops, and membership points), opportunities to collaborate with friends to earn rewards, and various shopping activities
based on their unique preferences, needs, and shopping context. For example, by using detailed individual consumer records
and corresponding webpage browsing and consumption analysis, Amazon and Alibaba offer consumers specific and tailored
discounts and promotions to induce purchases when consumers shop online (Zhu et al., 2017). Thus, this study proposes that:

H3: Personalized incentives positively influences perceived shopping effectiveness.

Flow is an intrinsic motivation that refers to the positive state of consciousness that users experience when they are deeply
involved in an activity (Ameen et al., 2020). The state of flow occurs when a consumer is so immersed in a compelling
experience. In the flow state, consumers’ awareness become concentrated on the activity itself, completely absorbed in the
activity while feeling that they have control over their environment (Csikszentmihalyi, 2000). According to this definition,
consumers are in a state of flow during shopping process with omnichannel when they are completely focused on shopping and
nothing else (e.g., lose track of time, lose self-consciouness about other activities). Prior research studies have emphasised the
importance of flow in understanding interactions between humans and technology (Su et al., 2016). As omnichannel
encompasses techonological features, the flow may serve as a key motivation for its adoption. Consumers can combine mobile
apps with in-store shopping, providing flow of continuous shopping experiences to customers. Researchers have examined
consumers’ shopping experiences through omnichannel using flow as a construct for measuring potential consumer experience
(Ameen et al., 2020). Therefore, this study proposes that the deeply immersed state of flow of omnichannel will make
customers perceive greater positive shopping values with omnichannel. Therefore, this study hypothesizes:

H4: Flow positively influences perceived shopping effectiveness.

Enjoyment is the extent to which the user of a product perceives the activity itself to be enjoyable, without consideration of any
outcomes or gains that may be expected from performing the activity (Davis et al., 1992). According to this definition of
enjoyment, activities are performed solely for the pure enjoyment or fun, excitement, relaxation gained from the activity.
Based on the original definition, this study defined enjoyment as the degree of pleasure that consumers expect to obtain from
using omnichannel for their single purchase. Enjoyment is distinct from the flow. Unlike enjoyment, which results from the
pleasure of activities, flow shows intense concentration, and a sense of being in control of the activities and can result from
self-directed activities which are not neccessary to bring pleasure. Past researchers have shown that enjoyment has a significant
effect on technology acceptance (Liu et al., 2015). For example, Liu et al. (2015) found that positive behaviors toward mobile
coupons are formed because consumers feel pleasure (enjoyment) in the act of using mobile coupons itself, and Xu et al. (2014)
showed that when consumers perceive greater enjoyment, they are more likely to engage with the technology system actively
and positively evaluate it. Gao et al. (2015) demonstrated that enjoyment positively influences a consumer attitude towards
smart medical devices. Given the proliferation of IT enabled-hedonic characteristics when shopping with omnichannel,
enjoyment could be a critical factor for the customer perception of shopping effectiveness with omnichannel. This study argues
that if consumers judge the activity of using a retailer’s omnichannel for their shopping to be interesting, they are more likely
to engage in shopping experiences with omnichannel and realize more shopping values. Therefore, this study hypothesizes:

H5: Enjoyment positively influences perceived shopping effectiveness.

Opportunity
Opportunity refers to the extent to which an individual can engage in a particular behaviour without restrictions (MacInnis et
al., 1991). It reflects the situational factors that either strengthen or hinders an individual’s behaviour. Opportunity often
includes both positive and negative perspectives of resource availability and impediments for achieving the desired behaviours.
Due to the benefits of omnichannel, retailers may put efforts into channel integration quality to facilitate customer shopping
(Hossain et al., 2020; Mirzabeiki & Saghiri, 2020). Omnichannel integration quality as a hierarchical construct consisting of
channel-service configuration with three sub-dimensions, content consistency with two sub-dimensions, process consistency
with two sub-dimensions, and assurance quality with three sub-dimensions (Hossain et al., 2020). Channel-service
configuration refers to channel performance in terms of providing services at the same level of quality and consistency
(Banerjee, 2014). It is mainly the association between services and channels within a firm (Sousa & Voss, 2006). Channel-
service configuration consists of three sub-dimensions including breadth of channel, transparency of channels, and

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appropriateness of channels. Breadth of channel refers to the availability of different channels through which a customer can
avail various services (Lee et al., 2019). Transparency of channels refers to customer knowledge of existing channels. It is
related to the awareness level of customers regarding the available channels and channel capabilities of the firm (Sousa & Voss,
2006). Appropriateness of channels refers to the suitability of the channel in providing the service (Banerjee, 2014). This study
proposes that facilitating channel-service configuration enables customers to take advantages of omnichannel and perform their
shopping effectively. Thus, the following hypothesis is proposed:

H6: Channel-service configuration positively influences perceived shopping effectiveness.

Content consistency refers to the consistency of outgoing and incoming information through different channels of the firm (Lee
et al., 2019). Content consistency consists of information consistency and transaction data integration as its sub-dimensions.
Information consistency refers to the consistency and uniformity of information within all the service delivery channels of the
firm (Banerjee, 2014). Transaction data integration refers to collecting customers' transaction data and integrating it within all
the channels to provide seamless service (Banerjee, 2014). This approach lets customers be easy to manage their purchase
records and quickly access their purchase history, thus facilitating their future purchase decisions. Therefore, customers are
highly appreciate for shopping values offered by consistently integrated transaction information. Thus, the following
hypothesis is proposed:

H7: Content consistency positively influences perceived shopping effectiveness.

Process consistency is related to service design, which refers to the consistency of various customer-facing elements that are
relevant and comparable within different channels. Service's feel, waiting time, image, employee discretion level gauge the
quality of process consistency (Banerjee, 2014). This research identifies system consistency and image consistency as sub-
dimensions of process consistency. System consistency, which is derived from electronic service quality research and
information systems refers to the technical issues of service delivery process, which are required to ensure all the channels of
the firm perform at a consistent level (Akter et al., 2016). Image consistency refers to consistent use of the store's brand name,
logo, slogan, and color within all the channels (Oh & Teo, 2010). To ensure image consistency, ambient cues of a physical
facility such as logo, surrounding colors, music, and overall feel should be reflected through typesetting, graphics, and display
colors in websites and mobile apps (White et al., 2013). This approach signals customers to trust the retailer’s capability of
providing services/products with consistent quality, thus encouraging customers to engage more in omnichannel for their
shopping goals. Thus, the following hypothesis is proposed:

H8: Process consistency positively influences perceived shopping effectiveness.

Assurance quality refers to different channel attributes that convey confidence and trust within customers. Assurance of service
while using multichannel has been conceptualized as a dimension of channel integration by Hossain et al. (2019). Furthermore,
this research conceptualizes Assurance quality through qualitative data analysis, and it confirms privacy, security, and service
recovery accessibility within all the channels is required to ensure Assurance quality. Privacy and Security have been
researched expansively within e-service quality research (Yoo & Donthu, 2001). Service recovery accessibility refers to
offering customers with channels and incorporated systems through which they can conveniently raise their service-related
issues to the firm. Research related to service recovery has always emphasized gathering customer feedback (Van Vaerenbergh
& Orsingher, 2016). Collecting customer feedback is vital for organizations, as without that service recovery cannot be even
attempted. Utilizing different channels easily informs service issues contributes to customers’ shopping effectiveness. Thus,
the following hypothesis is proposed:

H9: Assurance quality positively influences perceived shopping effectiveness.

Ability
Ability is the extent to which consumers have the necessary resources (e.g. knowledge, intelligence, money) to make an
desired outcome happen (MacInnis et al., 1991). As all customers are not equally equipped to engage with omnichannel, thus
the knowledge or expertise of relevant products or technology can guide them in the assessment of the benefits of shopping
with omnichannel. Without the necessary skills, even a motivated customer may not adopt omnichannel. Thus, this study
considers technology readiness (Parasuraman, 2000), which refers to an individual’s propensity to embrace new technology, as
a critical customer belief for adopting omnichannel. Past research studies note that high technology readiness enables
customers to understand the benefits of new technology better and operate it more efficiently (Blut & Wang, 2020). Along with
customers’ technology readiness, previous researchers have noted that psychological resources such as self-efficacy referring
to confidence in customers’ skills or proficiencies in using omnichannel for shopping can facilitate customer adoption of

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omnichannel (Van Nguyen et al., 2022). High self-efficacy consumers are likely to be more confident in their customers’ skills
or proficiencies to use omnichannel for a single purchase. They feel comfortable in using omnichannel and realize the value of
shopping with omnichannel. In contrast, low self-efficacy consumers feel less comfortable in using omnichannel and thus find
no incentive to adopt omnichannel. Formally stated, this study proposes the following hypotheses:

H10: Technology readiness positively influences perceived shopping effectiveness.

H11: Self-efficacy positively influences perceived shopping effectiveness.

Perceived Shopping Effectiveness and Omnichannel Usage Continuance Intention

Perceived shopping effectiveness refers to the extent to which customers perceive omnichannel brings shopping values and
enhances customers’ overall shopping experience. Shopping effectiveness is determined by the extent to which customers can
perform effectively their shopping using omnichannel. Yang and Forney (2013) demonstrated that the customer believes that
shopping with mobile will facilitate them in achieving the tasks which is positively related to the intentions to continue using it.
A customer experience is considered as high-quality if it can support customers to efficiently acquire products and services
(Chen & Yang, 2021)) and derive entertainment and pleasure from shopping (Barari et al., 2020). Therefore, this study
proposes that continuously receiving values and superior customer experiences from shopping with omnichannel attracts
consumers continue using the retailer’s omnichannel. Formally stated, this study proposes the following hypothesis:

H12: Perceived shopping effectiveness positively influences omnichannel usage continuance intention.

METHODOLOGY

Sample and Data Collection

To test the conceptual model and hypotheses, a quantitative method with survey data was used. Data will be gathered from an
omnichannel retailer’s customers in US via Amazon MTurk. To ensure that the respondents who participated in the survey had
actual experience using a retailer’s omnichannel services, they were asked whether they had had omnichannel shopping
experiences with this retailer at the beginning of the survey. Only the respondents who confirmed their previous experience
with this omnichannel retailer were allowed to complete the rest of the survey. The convenience sampling strategy will be
adopted to collect data. The intended sample size is around 500 valid responses.

Items and Measurement Validation

All of the measurement items in this study will be adopted from prior studies and modified to suit this research context. The
ten dimensions of channel integration quality will be adapted from Hossain et al. (2020). The technology readiness and self-
efficacy will be adapted from Rosenbaum and Wong (2015), Van Beuningen et al. (2009), respectively. Relative advantage,
perceived ease of use, personalized incentives, enjoyment and flow will be adapted from Kim et al. (2022), Kucukusta et al.
(2014), Hsia et al. (2020), Dabholkar and Bagozzi (2002), and Wang (2015), respectively. Perceived shopping effectiveness
will be adapted from Gao and Bai (2014). Omnichannel usage intention will be adapted from Shi et al. (2020). All
measurement items will be scored on 7-point Likert scales.

Hypothesis Testing
Structural equation modeling will be used to examine the conceptual model and hypotheses via SmartPls software. Finally, to
explore the influencing mechanisms of MOA variables on omnichannel usage continuance intention in detail, a bootstrapping
analysis with 5000 repetitions will be performed to test the mediating role of the perceived shopping effectiveness within 95%
bias-corrected confidence intervals.

RESULTS

Expected Contributions
Scholars and practitioners have paid close attention to omnichannel retailing strategies (Mishra et al., 2021). This study
contributes to the emerging omnichannel marketing literature in the following points. First, the findings offer new insight into
the salience of the shopping effectiveness regarding omnichannel shopping. The extant literature has identified the important
role of an effective omnichannel strategy in loyalty (Chen et al., 2022), product purchases (Bleier et al., 2019), and word of
mouth (Rodríguez-Torrico et al., 2021), all of which can create a company’s competitive advantage. However, no studies have
so far focused on the optimization of the shopping effectiveness in omnichannel environments. By adopting the MOA theory,
this study investigates the extent to which motivation, opportunity, and ability variables can boost customers’ perception of
shopping effectiveness of omnichannel. This study answers the call of Lemon and Verhoef (2016) to go beyond the widely
available channel choice models and develop a holistic omnichannel understanding customers’ shopping needs to enable its
shopping effectiveness. Second, studies have based on channel integration as a significant antecedent of customers’
perceptions and behaviors, including their perceived fluency (Shen et al., 2018), customer engagement and word of mouth (Lee
et al., 2019), customer empowerment (Zhang et al., 2018), satisfaction (Lee, 2020), perceived value (Hamouda, 2019), and
cross-buying intention (Hossain et al., 2020). Yet research exploring how channel integration quality shapes the shopping
effectiveness in an omni-channel shopping context is largely unavailable. This study examines the relationship between

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channel integration quality and perceived shopping effectiveness. The empirical findings from this study may offer additional
information regarding the influential mechanisms of channel integration quality newly reconceptualized (Hossain et al., 2020)
to fill the research gap regarding the association between channel integration quality and shopping effectiveness. Thus, this
study responds to researchers’ calls to empirically test the channel integration quality viewed as a new approach of marketing
concept in helping customers’ shopping effectiveness (Homburg et al., 2017). In addition, this study elucidates the mediation
effects of MOA variables on omnichannel usage intention through perceived shopping effectiveness. Third, previous studies
have conceptualized channel integration as a multidimensional construct that plays a fundamental role in improving customer
shopping experience and company performance (Hossain et al., 2020). However, the literature to date has focused exclusively
on the effects of channel integration without comparing the effectiveness among the dimensions. By examining the four main
dimensions with ten sub-dimensions of channel integration practices proposed by Hossain et al. (2020), this study may verify
the usefulness of these metrics. Therefore, this study provides a more holistic view of the effects of channel integration quality
in omnichannel retailing environments.

REFERENCES
Adivar, B., Hüseyinoğlu, I. Ö. Y., & Christopher, M. (2019). A quantitative performance management framework for assessing
omnichannel retail supply chains. Journal of Retailing and Consumer Services, 48, 257-269.
https://doi.org/10.1016/j.jretconser.2019.02.024
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art
and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749.
DOI: 10.1109/TKDE.2005.99
Akter, S., Wamba, S. F., & D’Ambra, J. (2019). Enabling a transformative service system by modeling quality
dynamics. International Journal of Production Economics, 207, 210-226. https://doi.org/10.1016/j.ijpe.2016.08.025
Alizila, 2018. How Working With Alibaba Has Changed Retailer Intime. Retrieved from https://www.alizila.com/how-
working-with-alibaba-has-changed-chinese-retailer-intime/(accessed 20 Sep 2022)
Ameen, N., Tarhini, A., Shah, M. and Madichie, N.O. (2021), "Going with the flow: smart shopping malls and omnichannel
retailing", Journal of Services Marketing, 35(3), 325-348. https://doi.org/10.1108/JSM-02-2020-0066
Asare, C., Majeed, M., & Cole, N. A. (2022). Omnichannel Integration Quality, Perceived Value, and Brand Loyalty in the
Consumer Electronics Market: The Mediating Effect of Consumer Personality. In Advances in Information
Communication Technology and Computing (pp. 29-45). Springer, Singapore.
Asmare, A., & Zewdie, S. (2022). Omnichannel retailing strategy: a systematic review. The International Review of Retail,
Distribution and Consumer Research, 32(1), 59-79. https://doi.org/10.1080/09593969.2021.2024447
Balaji, M. S., & Roy, S. K. (2017). Value co-creation with Internet of things technology in the retail industry. Journal of
Marketing Management, 33 (1–2), 7–31. https://doi.org/10.1080/0267257X. 2016.1217914
Banerjee, M. (2014). Misalignment and its influence on integration quality in multichannel Services. Journal of Service
Research, 17(4), 460–474.
Barari, M., Ross, M., & Surachartkumtonkun, J. (2020). Negative and positive customer shopping experience in an online
context. Journal of Retailing and Consumer Services, 53, 1–9. https://doi.org/10.1016/j.jretconser.2019.101985.
Bleier, A., Harmeling, C. M., & Palmatier, R. W. (2019). Creating effective online customer experiences. Journal of Marketing,
83(2), 98–119. https://doi.org/ 10.1177/0022242918809930.
Blut, M., & Wang, C. (2020). Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on
technology usage. Journal of the Academy of Marketing Science, 48(4), 649-669.
Chen, C. S., Lu, H. P., & Luor, T. (2018). A new flow of Location Based Service mobile games: Non-stickiness on Pokémon
Go. Computers in Human Behavior, 89, 182-190.
Chen, N., & Yang, Y. (2021). The impact of customer experience on consumer purchase intention in cross-border E-
commerce——Taking network structural embeddedness as mediator variable. Journal of Retailing and Consumer
Services, 59, 102344.
Chen, X., Su, X., Li, Z., Wu, J., Zheng, M., & Xu, A. (2022). The impact of omni-channel collaborative marketing on
customer loyalty to fresh retailers: the mediating effect of the omni-channel shopping experience. Operations
Management Research, 1-15.
Collins, K. (2019). Omnichannel marketing automation statistics for 2019. ClickZ. Published April 9, 2019.
https://www.clickz.com/onmichannel-marketing-automati on-statisticsfor-2019/231381-2/231381/. (accessed 20 Sep
2022)
Csikszentmihalyi, Mihaly. Beyond boredom and anxiety. Jossey-bass, 2000.
Cui, X., Lai, V. S., Lowry, P. B., & Lei, Y. (2020). The effects of bidder factors on online bidding strategies: a motivation-
opportunity-ability (MOA) model. Decision Support Systems, 138, 113397.
Cummins, S., Peltier, J. W., & Dixon, A. (2016). Omni-channel research framework in the context of personal selling and sales
management: A review and research extensions. Journal of Research in Interactive Marketing.
Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: Moderating effects of
consumer traits and situational factors. Journal of the Academy of Marketing Science, 30 (3), 184–201.
http://dx.doi.org/10.1177/0092070302303001

The 22nd International Conference on Electronic Business, Bangkok, Thailand, October 13-17, 2022
7
 Pham

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace
1. Journal of applied social psychology, 22(14), 1111-1132.
Frow, P., & Payne, A. (2007). Towards the ‘perfect’ customer experience. Journal of Brand Management, 15(2), 89–101.
https://doi.org/10.1057/palgrave.bm.2550120.
Gao, W., Fan, H., Li, W., & Wang, H. (2021). Crafting the customer experience in omnichannel contexts: The role of channel
integration. Journal of Business Research, 126, 12-22.
Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial
Management & Data Systems, 115 (9), 1704–1723. http://dx.doi.org/10.1108/IMDS-03- 2015-0087
Gruen, T. W., Osmonbekov, T., & Czaplewski, A. J. (2005). How e-communities extend the concept of exchange in marketing:
An application of the motivation, opportunity, ability (MOA) theory. Marketing Theory, 5(1), 33-49.
Hamouda, M. (2019). Omni-channel banking integration quality and perceived value as drivers of consumers’ satisfaction and
loyalty. Journal of Enterprise Information Management, 32(4), 608–625. https://doi.org/10.1108/JEIM-12-2018-0279.
Homburg, C., Jozic, D., & Kuehnl, C. (2017). Customer experience management: Toward implementing an evolving
marketing concept. Journal of the Academy of Marketing Science, 45(3), 377–401. https://doi.org/10.1007/s11747-015-
0460-7.
Hossain, T. M. T., Akter, S., Kattiyapornpong, U., & Dwivedi, Y. (2020). Reconceptualizing integration quality dynamics for
omnichannel marketing. Industrial Marketing Management, 87, 225-241.
Hsia, T. L., Wu, J. H., Xu, X., Li, Q., Peng, L., & Robinson, S. (2020). Omnichannel retailing: The role of situational
involvement in facilitating consumer experiences. Information & Management, 57(8), 103390.
Hsia, T. L., Wu, J. H., Xu, X., Li, Q., Peng, L., & Robinson, S. (2020). Omnichannel retailing: The role of situational
involvement in facilitating consumer experiences. Information & Management, 57(8), 103390.
Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow
experience. Information & management, 41(7), 853-868.
Huang, M. H. (2006). Flow, enduring, and situational involvement in the Web environment: A tripartite second‐order
examination. Psychology & Marketing, 23(5), 383-411.
Juaneda-Ayensa, E., Mosquera, A., & Sierra Murillo, Y. (2016). Omnichannel customer behavior: key drivers of technology
acceptance and use and their effects on purchase intention. Frontiers in psychology, 7, 1117
Kang, J. Y. M. (2019). What drives omnichannel shopping behaviors? Fashion lifestyle of social-local-mobile
consumers. Journal of Fashion Marketing and Management: An International Journal.
Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision
support systems, 43(1), 111-126.
Kim, S., Connerton, T. P., & Park, C. (2022). Transforming the automotive retail: Drivers for customers' omnichannel BOPS
(Buy Online & Pick up in Store) behavior. Journal of Business Research, 139, 411-425.
Kopot, C., & Cude, B. J. (2021). Channel depth or consistency? A study on establishing a sustainable omnichannel strategy for
fashion department store retailers. Sustainability, 13(13), 6993.
Kucukusta, D., Heung, V. C., & Hui, S. (2014). Deploying self-service technology in luxury hotel brands: Perceptions of
business travelers. Journal of Travel & Tourism Marketing, 31 (1), 55–70.
https://doi.org/10.1080/10548408.2014.861707
Lee, Z. W., Chan, T. K., Chong, A. Y. L., & Thadani, D. R. (2019). Customer engagement through omnichannel retailing: The
effects of channel integration quality. Industrial Marketing Management, 77, 90–101.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of
marketing, 80(6), 69-96.
Liu, F., Zhao, X., Chau, P. Y., & Tang, Q. (2015). Roles of perceived value and individual differences in the acceptance of
mobile coupon applications. Internet Research.
MacInnis, D. J., Moorman, C., & Jaworski, B. J. (1991). Enhancing and measuring consumers' motivation, opportunity, and
ability to process brand information from ads. The Journal of Marketing, 32-53.
McKinsey Digital. (2019). China digital consumer trends 2019. https://www.mckinsey. com/featured-insights/china/china-
digital-consumer-trends-in-2019 (accessed 20 Sep 2022)
Mirzabeiki, V., & Saghiri, S. S. (2020). From ambition to action: How to achieve integration in omni-channel?. Journal of
Business Research, 110, 1-11.
Mishra, R., Singh, R. K., & Koles, B. (2021). Consumer decision‐making in Omnichannel retailing: Literature review and
future research agenda. International Journal of Consumer Studies, 45(2), 147-174.
Oh, L.-B., & Teo, H.-H. (2010). Consumer value co-creation in a hybrid commerce service delivery system. International
Journal of Electronic Commerce, 14(3), 35–62.
Oliver Wyman. (2020). Thriving in the new normal. http://www.oliverwyman.cn /content/dam/oliver-
wyman/v2/publications/2020/may/thriving-in-the-new-n ormal_v2-cn.pdf.
Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new
technologies. Journal of service research, 2(4), 307-320.
Rodríguez-Torrico, P., San José Cabezudo, R., San-Martín, S. and Trabold Apadula, L. (2021), "Let it flow: the role of
seamlessness and the optimal experience on consumer word of mouth in omnichannel marketing", Journal of Research
in Interactive Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JRIM-06-2021-0154
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.

The 22nd International Conference on Electronic Business, Bangkok, Thailand, October 13-17, 2022
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 Pham

Rosenbaum, M. S., & Wong, I. A. (2015). If you install it, will they use it? Understanding why hospitality customers take
“technological pauses” from self-service technology. Journal of Business Research, 68 (9), 1862–1868.
http://dx.doi.org/10.1016/j.jbusres.2015.01.014
Roy, S. K., Balaji, M. S., Sadeque, S., Nguyen, B., & Melewar, T. C. (2017). Constituents and consequences of smart customer
experience in retailing. Technological Forecasting and Social Change, 124, 257-270.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary
educational psychology, 25(1), 54-67.
Sabnis, G., Chatterjee, S. C., Grewal, R., & Lilien, G. L. (2013). The sales lead black hole: On sales reps’ follow-up of
marketing leads. Journal of Marketing, 77 (1), 52–67. http://dx.doi.org/10.1509/jm.10.0047
Shen, X. L., Li, Y. J., Sun, Y., & Wang, N. (2018). Channel integration quality, perceived fluency and omnichannel service
usage: The moderating roles of internal and external usage experience. Decision Support Systems, 109, 61-73.
Shi, S., Wang, Y., Chen, X., & Zhang, Q. (2020). Conceptualization of omnichannel customer experience and its impact on
shopping intention: A mixed-method approach. International Journal of Information Management, 50, 325–336.
https://doi. org/10.1016/j.ijinfomgt.2019.09.001.
Sousa, R., & Voss, C. A. (2006). Service quality in multichannel services employing virtual channels. Journal of Service
Research, 8(4), 356–371.
Su, Y. S., Chiang, W. L., Lee, C. T. J., & Chang, H. C. (2016). The effect of flow experience on player loyalty in mobile game
application. Computers in Human Behavior, 63, 240-248.
Van Beuningen, J., de Ruyter, K., Wetzels, M., & Streukens, S. (2009). Customer self-efficacy in technology-based self-
service assessing between-and within-person differences. Journal of Service Research, 11 (4), 407–428.
https://doi.org/10.1177/1094670509333237
Van Nguyen, A. T., McClelland, R., & Thuan, N. H. (2022). Exploring customer experience during channel switching in
omnichannel retailing context: A qualitative assessment. Journal of Retailing and Consumer Services, 64, 102803.
Van Vaerenbergh, Y., & Orsingher, C. (2016). Service recovery: An integrative framework and research agenda. Academy of
Management Perspectives, 30(3), 328-346.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the
technology acceptance model. Information systems research, 11(4), 342-365.
Vietnamnet, 2020. COVID accelerates shift towards omni-channel retail. Retreived from https://vietnamnet.vn/en/covid-
accelerates-shift-towards-omni-channel-retail-659250.html
Wang, C., Harris, J., & Patterson, P. (2013). The roles of habit, self-efficacy, and satisfaction in driving continued use of self-
service technologies: a longitudinal study. Journal of Service Research, 16(3), 400-414.
Wang, K. (2015). Determinants of mobile value-added service continuance: The mediating role of service
experience. Information & Management, 52(3), 261-274.
White, R. C., Joseph-Mathews, S., & Voorhees, C. M. (2013). The effects of service on multichannel retailers’ brand equity.
Journal of Services Marketing, 27(4), 259–270.
Xu, J., Benbasat, I., & Cenfetelli, R. T. (2014). Research note-the influences of online service technol- ogies and task
complexity on efficiency and personalization. Information Systems Research, 25 (2), 420–436.
http://dx.doi.org/10.1287/isre.2013.0503
Yang, K., & Forney, J. C. (2013). The moderating role of consumer technology anxiety in mobile shopping adoption:
differential effects of facilitating conditions and social influences. Journal of Electronic Commerce Research, 14(4), 334.
Yoo, B., & Donthu, N. (2001). Developing a scale to measure the perceived quality of an internet shopping site (SITEQUAL).
Quarterly Journal of Electronic Commerce, 2(1), 31–45.
Zhu, H., Ou, C. X., van den Heuvel, W. J. A., & Liu, H. (2017). Privacy calculus and its utility for personalization services in
e-commerce: An analysis of consumer decision-making. Information & Management, 54(4), 427-437.
Zhu, H., Ou, C. X., van den Heuvel, W. J. A., & Liu, H. (2017). Privacy calculus and its utility for personalization services in
e-commerce: An analysis of consumer decision-making. Information & Management, 54(4), 427-437.

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